{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make datasets and test set\n",
    "#### '<font color=\"blue\">+</font>' points are sampled from a gaussian distribution (X, Y) ~ N(3, 6, 1, 1, 0).<br>'<font color=\"green\">o</font>' points are sampled from a gaussian distribution (X, Y) ~ N(6, 3, 1, 1, 0)<br>make sure the shape of the data matrix with which you feed to the tensor correspond to the shape of the tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b64018d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "dot_num = 100\n",
    "x_p = np.random.normal(3., 1, dot_num)\n",
    "y_p = np.random.normal(6., 1, dot_num)\n",
    "y = np.ones(dot_num)\n",
    "C1 = np.array([x_p, y_p, y]).T\n",
    "\n",
    "x_n = np.random.normal(6., 1, dot_num)\n",
    "y_n = np.random.normal(3., 1, dot_num)\n",
    "y = np.zeros(dot_num)\n",
    "C2 = np.array([x_n, y_n, y]).T\n",
    "\n",
    "plt.scatter(C1[:, 0], C1[:, 1], c='b', marker='+')\n",
    "plt.scatter(C2[:, 0], C2[:, 1], c='g', marker='o')\n",
    "\n",
    "data_set = np.concatenate((C1, C2), axis=0)\n",
    "np.random.shuffle(data_set)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the graph and run it\n",
    "#### In Graph building stage, python call tensorflow api to create Tensorflow Nodes and connect those Nodes. (at this moment the graph is static)<br>Note that in tensorflow Graph, <FONT COLOR=\"green\">Nodes</FONT> are called <FONT COLOR=\"green\">Operations</FONT> and <FONT COLOR=\"red\">Edges</FONT> connect those Nodes are called <font color=\"red\">tensors</font> \n",
    "#### A fetch on a tensor would require its preceeding Node to activate,  for a Node to activate would require all it's inbound tensors/Edges to be filled with data, recursively all the way to the leaf Nodes (placeholder, variable, constant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "################Graph building stage################\n",
    "# Define placeholders for input\n",
    "X = tf.placeholder(tf.float32, shape=(None, 2))\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1)) \n",
    "\n",
    "# Define variables to be learned\n",
    "with tf.variable_scope(\"Logistic_regression\"):\n",
    "    W = tf.Variable(np.random.rand(2, 1), 'weight', dtype=tf.float32)\n",
    "    b = tf.Variable(np.random.rand(1, 1), 'bias', dtype=tf.float32)\n",
    "    \n",
    "    logits = tf.matmul(X, W) + b\n",
    "    pred = tf.sigmoid(logits)\n",
    "    loss = tf.nn.sigmoid_cross_entropy_with_logits(logits, y)\n",
    "    loss = tf.reduce_mean(loss)\n",
    "#     y_hat = tf.sigmoid(logits)\n",
    "#     loss = tf.reduce_mean(y * -tf.log(y_hat+1e-12) + (1 - y) * -tf.log(1 - y_hat+1e-12)) #cross entropy\n",
    "\n",
    "opt = tf.train.AdamOptimizer(0.01)\n",
    "opt_operation = opt.minimize(loss)\n",
    "####################end of building graph###########\n",
    "animation_fram = []\n",
    "\n",
    "################Run/fetch the graph#################\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    for _ in xrange(500):\n",
    "        _, loss_val = sess.run([opt_operation, loss], feed_dict={X: data_set[:, :2], y: data_set[:, 2].reshape((-1, 1))})\n",
    "        W_opt, b_opt = sess.run([W, b])\n",
    "        animation_fram.append((W_opt[0, 0], W_opt[1, 0], b_opt, loss_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the fitting process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=\"432.0\" height=\"288.0\" controls autoplay loop>\n",
       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAHGJ21kYXQAAAKvBgX//6vcRem9\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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7RJN9pMHg1GHbNrj/fvjySxg3Dnr00JoBp1BjkASEeqoPRxF79/F+VpYAal5e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\ngElw9+BwRCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a9006c250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f, ax = plt.subplots(figsize=(6,4))\n",
    "f.suptitle('Logistic Regression Example', fontsize=15)\n",
    "plt.ylabel('Y')\n",
    "plt.xlabel('X')\n",
    "ax.set_xlim(0, 10)\n",
    "ax.set_ylim(0, 10)\n",
    "\n",
    "line_d, = ax.plot([], [], label='fit_line')\n",
    "C1_dots, = ax.plot([], [], '+', c='b', label='actual_dots')\n",
    "C2_dots, = ax.plot([], [], 'o', c='g' ,label='actual_dots')\n",
    "\n",
    "\n",
    "frame_text = ax.text(0.02, 0.95,'',horizontalalignment='left',verticalalignment='top', transform=ax.transAxes)\n",
    "# ax.legend()\n",
    "\n",
    "def init():\n",
    "    line_d.set_data([],[])\n",
    "    C1_dots.set_data([],[])\n",
    "    C2_dots.set_data([],[])\n",
    "    return (line_d,) + (C1_dots,) + (C2_dots,)\n",
    "\n",
    "def animate(i):\n",
    "    xx = np.arange(10, step=0.1)\n",
    "    a = animation_fram[i][0]\n",
    "    b = animation_fram[i][1]\n",
    "    c = animation_fram[i][2]\n",
    "    yy = a/-b * xx +c/-b\n",
    "    line_d.set_data(xx, yy)\n",
    "        \n",
    "    C1_dots.set_data(C1[:, 0], C1[:, 1])\n",
    "    C2_dots.set_data(C2[:, 0], C2[:, 1])\n",
    "    \n",
    "    frame_text.set_text('Timestep = %.1d/%.1d\\nLoss = %.3f' % (i, len(animation_fram), animation_fram[i][3]))\n",
    "    \n",
    "    return (line_d,) + (C1_dots,) + (C2_dots,)\n",
    "\n",
    "anim = animation.FuncAnimation(f, animate, init_func=init,\n",
    "                               frames=len(animation_fram), interval=30, blit=True)\n",
    "\n",
    "HTML(anim.to_html5_video())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
